Effect of icing roughness on wind turbine power production

Blasco, Peter Michael; Palacios, José; Schmitz, Sven

doi:10.1002/we.2026

Cited by 50 publications

(41 citation statements)

References 34 publications

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“…The power losses predicted at the rated wind speed are in the range of 18% to 31%, which is in agreement with the numerical predictions reported in the literature. () Similar power losses are also reported in the wind farms. ()…”

Section: Resultssupporting

confidence: 78%

“…In the second group validation cases, DU 93‐W‐210 blade profile, which is commonly used in pitch controlled wind turbines, is considered in the full scale and in a testing scale. The icing and flow conditions in the reference experimental study of Blasco et al are presented in Table . It should be noted that the exposure times in the scaled study is adjusted for similarity.…”

Section: Resultsmentioning

confidence: 99%

“…The icing and flow conditions in the reference experimental study of Blasco et al are presented in Table . It should be noted that the exposure times in the scaled study is adjusted for similarity. All simulations are performed in a multi‐step computation by using eight steps.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Predictions of ice formations on wind turbine blades and power production losses due to icing

2019

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Prediction of ice shapes on a wind turbine blade makes it possible to estimate the power production losses due to icing. Ice accretion on wind turbine blades is responsible for a significant increase in aerodynamic drag and decrease in aerodynamic lift and may even cause premature flow separation. All these events create power losses and the amount of power loss depends on the severity of icing and the turbine blade profile. The role of critical parameters such as wind speed, temperature, liquid water content on the ice shape, and size is analyzed using an ice accretion prediction methodology coupled with a blade element momentum tool. The predicted ice shapes on various airfoil profiles are validated against the available experimental and numerical data in the literature. The error in predicted rime and glime ice volumes and the maximum ice thicknesses varies between 3% and 25% in comparison with the experimental data depending on the ice type. The current study presents an efficient and accurate numerical methodology to perform an investigation for ice-induced power losses under various icing conditions on horizontal axis wind turbines. The novelty of the present work resides in a unified and coupled approach that deals with the ice accretion prediction and performance analysis of iced wind turbines. Sectional ice profiles are first predicted along the blade span, where the concurrence of both rime and glaze ice formations may be observed. The power loss is then evaluated under the varying ice profiles along the blade. It is shown that the tool developed may effectively be used in the prediction of power production losses of wind turbines at representative atmospheric icing conditions. KEYWORDS atmospheric icing, ice accretion, power losses, wind energy INTRODUCTIONIn recent years, producing energy from renewable energy sources is in great demand and installation of wind turbines in the world has been increasing steadily. Cold climate regions have the advantage of high wind speeds, high air density, and sparsely populated residential areas.However, wind turbines in these regions are usually exposed to atmospheric icing conditions, which have a detrimental effect on the performance.Formation of ice on wind turbine blades creates unwanted performance losses by changing blade aerodynamic characteristics and also reduces the lifetime of wind turbine components. 1,2 It is well-known that icing occurs when supercooled droplets in the atmosphere impinge on a surface such as a turbine blade. Liquid water content, temperature, droplet diameter, and wind speed are the basic factors affecting icing. Liquid water content mostly determines the severity of icing and its type. Wind speed and temperature also influence the type and intensity of ice. Droplets may either freeze instantly and form rime

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Section: Resultssupporting

confidence: 78%

Section: Resultsmentioning

confidence: 99%

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Predictions of ice formations on wind turbine blades and power production losses due to icing

2019

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“…Heavy icing can add hundreds of kilograms to the mass of the wind turbine nacelle and its blades, and it has more significant effects on the wind turbine blades, which are rotary components, than on the nacelle. The additional mass changes the aerodynamic configuration of the blades, and it can lead to the reduction of power . Moreover, the mass imbalance due to the ice coating on different blades results in increased fatigue of components .…”

Section: Introductionmentioning

confidence: 99%

Icing estimation on wind turbine blade by the interface temperature using distributed fiber optic sensors

Zhang

Zhou

2020

Struct Control Health Monit

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Summary Icing on the surface of wind turbine blades is one of the most critical problems for wind turbines installed at sites in cold climate. It can significantly affect the performance of the entire wind turbine. A number of deicing methods and techniques have been developed to address this problem. However, first of all, timely icing detection and estimation can provide the necessary information, such as the amount and location of icing, which are very useful for optimizing the deicing process. In this work, first, the thermodynamic processes during icing on the surface of wind turbine blades were presented to understand the characteristics of the varying temperature related to icing process. In the experimental section, a wind turbine blade model was tested in the low‐temperature laboratory. High‐performance distributed fiber optic sensors were attached to the surface of the wind turbine blade to measure the temperature change. This sensor can provide the temperature information as much as possible, which enables the identification of the initiation time of icing and the estimation of the amount of icing. As a result, the relationship between the amount of icing and the temperature change can be obtained. It indicates that the proposed estimation method is efficient and sensitive for ice monitoring of wind turbine blades. Finally, finite element analysis was implemented to explore the effects of airflow on the temperature curves.

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“…Shu et al studied the characteristics of leaf icing and the severity of icing on the power characteristics of wind turbines under natural icing conditions [9]. Blasco et al performed a quantitative analysis of the power loss of a representative 1.5 MW wind turbine under various icing conditions, attempting to reduce the loss of wind farms in cold regions by formulating some control strategies [10]. Based on the analysis of supervisory control and data acquisition (SCADA) data, Li et al proposed a method for detection of blade icing based on logistic regression [11].…”

Section: Introductionmentioning

confidence: 99%

Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier

Zhang

Wang

et al. 2018

Energies

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When wind turbine blades are icing, the output power of a wind turbine tends to reduce, thus informing the selection of two basic variables of wind speed and power. Then other features, such as the degree of power deviation from the power curve fitted by normal sample data, are extracted to build the model based on the random forest classifier with the confusion matrix for result assessment. The model indicates that it has high accuracy and good generalization ability verified with the data from the China Industrial Big Data Innovation Competition. This study looks at ice detection on wind turbine blades using supervisory control and data acquisition (SCADA) data and thereafter a model based on the random forest classifier is proposed. Compared with other classification models, the model based on the random forest classifier is more accurate and more efficient in terms of computing capabilities, making it more suitable for the practical application on ice detection.

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Effect of icing roughness on wind turbine power production

Cited by 50 publications

References 34 publications

Predictions of ice formations on wind turbine blades and power production losses due to icing

Predictions of ice formations on wind turbine blades and power production losses due to icing

Icing estimation on wind turbine blade by the interface temperature using distributed fiber optic sensors

Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier

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